
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor

class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			def __init__(							self			:							Union[str, Any]    ,						snake_case_			:							Dict    ,						snake_case_			:							Optional[Any]=7    ,						snake_case_			:							List[str]=3    ,						snake_case_			:							Union[str, Any]=18    ,						snake_case_			:							int=30    ,						snake_case_			:							Dict=400    ,						snake_case_			:							Tuple=True    ,						snake_case_			:							List[Any]=None    ,						snake_case_			:							Optional[int]=True    ,						snake_case_			:							List[str]=None    ,						snake_case_			:							Tuple=True    ,						):
							UpperCamelCase_:				Any						=    size if size is not None else {"""shortest_edge""": 20}
							UpperCamelCase_:				str						=    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:				Tuple						=    parent
							UpperCamelCase_:				str						=    batch_size
							UpperCamelCase_:				Union[str, Any]						=    num_channels
							UpperCamelCase_:				str						=    image_size
							UpperCamelCase_:				int						=    min_resolution
							UpperCamelCase_:				List[str]						=    max_resolution
							UpperCamelCase_:				List[Any]						=    do_resize
							UpperCamelCase_:				int						=    size
							UpperCamelCase_:				int						=    do_center_crop
							UpperCamelCase_:				str						=    crop_size
							UpperCamelCase_:				Tuple						=    do_flip_channel_order




			def    lowerCAmelCase__						(							self			:							Tuple   ):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_flip_channel_order": self.do_flip_channel_order,
							}




@require_torch
@require_vision
class 							_UpperCamelCase					(       _A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    Optional[Any]          =			MobileViTImageProcessor if is_vision_available() else None

			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				int						=    MobileViTImageProcessingTester(self   )

			@property
			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							return self.image_processor_tester.prepare_image_processor_dict()

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				Tuple						=    self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(snake_case_    ,						"""do_resize"""   )   )
							self.assertTrue(hasattr(snake_case_    ,						"""size"""   )   )
							self.assertTrue(hasattr(snake_case_    ,						"""do_center_crop"""   )   )
							self.assertTrue(hasattr(snake_case_    ,						"""center_crop"""   )   )
							self.assertTrue(hasattr(snake_case_    ,						"""do_flip_channel_order"""   )   )

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							UpperCamelCase_:				int						=    self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size    ,						{"""shortest_edge""": 20}   )
							self.assertEqual(image_processor.crop_size    ,						{"""height""": 18, """width""": 18}   )

							UpperCamelCase_:				List[str]						=    self.image_processing_class.from_dict(self.image_processor_dict    ,						size=42    ,						crop_size=84   )
							self.assertEqual(image_processor.size    ,						{"""shortest_edge""": 42}   )
							self.assertEqual(image_processor.crop_size    ,						{"""height""": 84, """width""": 84}   )

			def    lowerCAmelCase__						(							self			:							Dict   ):
							pass

			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							# Initialize image_processing
							UpperCamelCase_:				Optional[Any]						=    self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							UpperCamelCase_:				int						=    prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=snake_case_   )
							for image in image_inputs:
											self.assertIsInstance(snake_case_    ,						Image.Image   )

							# Test not batched input
							UpperCamelCase_:				Optional[int]						=    image_processing(image_inputs[0]    ,						return_tensors="""pt"""   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )    ,						)

							# Test batched
							UpperCamelCase_:				Dict						=    image_processing(snake_case_    ,						return_tensors="""pt"""   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )    ,						)

			def    lowerCAmelCase__						(							self			:							Dict   ):
							# Initialize image_processing
							UpperCamelCase_:				Dict						=    self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							UpperCamelCase_:				str						=    prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=snake_case_    ,						numpify=snake_case_   )
							for image in image_inputs:
											self.assertIsInstance(snake_case_    ,						np.ndarray   )

							# Test not batched input
							UpperCamelCase_:				str						=    image_processing(image_inputs[0]    ,						return_tensors="""pt"""   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )    ,						)

							# Test batched
							UpperCamelCase_:				Union[str, Any]						=    image_processing(snake_case_    ,						return_tensors="""pt"""   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )    ,						)




			def    lowerCAmelCase__						(							self			:							List[str]   ):
							# Initialize image_processing
							UpperCamelCase_:				Dict						=    self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							UpperCamelCase_:				Any						=    prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=snake_case_    ,						torchify=snake_case_   )
							for image in image_inputs:
											self.assertIsInstance(snake_case_    ,						torch.Tensor   )

							# Test not batched input
							UpperCamelCase_:				Optional[Any]						=    image_processing(image_inputs[0]    ,						return_tensors="""pt"""   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )    ,						)

							# Test batched
							UpperCamelCase_:				Optional[Any]						=    image_processing(snake_case_    ,						return_tensors="""pt"""   ).pixel_values
							self.assertEqual(
							    encoded_images.shape    ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )    ,						)








def 							A__       (    lowerCamelCase = 4_00_00_00    )	->						int:
				UpperCamelCase_:				Dict						=    []
				UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    0, 1
				while b <= n:
								if b % 2 == 0:
												even_fibs.append(lowerCamelCase    )
								UpperCamelCase_, UpperCamelCase_:				int						=    b, a + b
				return sum(lowerCamelCase    )


if __name__ == "__main__":
							print(F"""{solution() = }""")






